import jittor.nn as nn
from ljp.cell import SubtractorConv2D, SubtractorLinear

Conv = SubtractorConv2D


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return Conv(in_planes, out_planes, kernel_size=3, stride=stride,
                padding=1, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return Conv(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride=stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU()
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def execute(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU()
        self.downsample = downsample
        self.stride = stride

    def execute(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, inchannels=3, alpha=0.01, ):
        super(ResNet, self).__init__()
        self.inplanes = 64
        self.alpha = alpha
        self.conv1 = Conv(inchannels, 64, kernel_size=7, stride=2, padding=3,
                          bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = Conv(512 * block.expansion, num_classes, 1)
        self.bn2 = nn.BatchNorm2d(num_classes)
        self.desc = "ResNet"

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forwardfunction(self, features, labels):
        out = self.execute(features)

        regterms = []
        for name, module in self.named_modules():
            if isinstance(module, SubtractorConv2D) or isinstance(module, SubtractorLinear):
                regterms.append(module.regterm)

        _batch_loss = self.loss_func(out, labels) + self.alpha * sum(regterms) / len(regterms)

        return out, _batch_loss

    def execute(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = self.fc(x)
        x = self.bn2(x)
        # print('ss',x.shape)

        return x.view(x.size(0), -1)


def resnet50_sub(**kwargs):
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    model.desc = 'ResNet50Sub'
    return model


def resnet18_sub(**kwargs):
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    model.desc = 'ResNet18Sub'
    return model
